---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- generated_from_trainer
datasets:
- improver/length_human_train.jsonl
model-index:
- name: data/user_data/riyaza/saved_models/DeepSeek-R1-Distill-Qwen-1.5B_demo
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.6.0`
```yaml
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
# optionally might have model_type or tokenizer_type
# model_type: AutoModelForCausalLM
# tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: improver/length_human_train.jsonl
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: /data/user_data/riyaza/saved_models/DeepSeek-R1-Distill-Qwen-1.5B_demo

sequence_len: 4096
sample_packing: false
pad_to_sequence_len:

adapter: qlora
lora_model_dir:
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: "DeepSeek-R1-Distill-Qwen-1.5B_demo"
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00005

train_on_inputs: false
group_by_length: true
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: /data/user_data/riyaza/saved_models/DeepSeek-R1-Distill-Qwen-1.5B_demo/checkpoint-344
local_rank:
logging_steps: 10
xformers_attention:
flash_attention:

warmup_steps: 100
evals_per_epoch: 1
eval_table_size:
eval_max_new_tokens: 256
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# data/user_data/riyaza/saved_models/DeepSeek-R1-Distill-Qwen-1.5B_demo

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the improver/length_human_train.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2708

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4873        | 0.9971 | 172  | 0.2755          |
| 0.3758        | 1.9913 | 344  | 0.2677          |
| 0.3732        | 2.9971 | 516  | 0.2708          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.21.0